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16. Changelog

2026-09-07 — Unified Kaggle Dataset & Colab Trainer Update

Dataset Pipeline & Colab Notebook: - Updated dataset download source to unified Kaggle dataset algsoch/breed-cattle-buffalo containing pre-structured cattle/ (57 breeds) and buffalo/ (18 breeds) subdirectories. - Simplified Kaggle download logic in colab/cattle_buffalo_trainer.py to extract directly into data/raw/, eliminating redundant file moving operations and outdated inline comments. - Regenerated colab/cattle_buffalo_trainer.ipynb from updated python script. - Updated project documentation across README.md, docs/, and knowledge base.

2026-09-06 — Hotfix: CUDA total_mem AttributeError

Bug Fix: - Fixed AttributeError: 'torch._C._CudaDeviceProperties' object has no attribute 'total_mem' that crashed §6 Training on Colab T4 - Root cause: PyTorch uses total_memory, not total_mem - Fixed in src/train.py (setup_device()) and both occurrences in colab/cattle_buffalo_trainer.py - Regenerated colab/cattle_buffalo_trainer.ipynb from fixed .py

Documentation: - Added Colab gotchas table to CONTEXT.md §14 covering: total_mem bug, GitHub clone cache issue, runtime restart behaviour - Added rm -rf /content/project before git clone in §14 best practices to ensure latest code is always used

2026-09-06 — Colab + SOTA Hyperparameters + Android QAT

Colab Training: - Created colab/ directory with full training notebook - 3 project setup options: GitHub clone, zip upload, Google Drive - 3 dataset options: Kaggle API, archive upload, Google Drive - Hyperparameter configuration cell with all tunable parameters - Image prediction cell for testing with uploaded images - Export & download: portable bundle + ONNX + INT8 - GPU memory monitor cell

SOTA Hyperparameters: - Switched from Adam → AdamW (weight_decay=1e-2) - Added label smoothing (0.1) to soft cross-entropy - Added linear warmup scheduler (3 epochs) before cosine annealing - Added gradient accumulation (2 steps, effective batch=128) - Increased batch size 32 → 64 - Optimized split ratio 80/10/10 → 85/10/5 - Phase 2 epochs 30 → 40, LR 1e-4 → 2e-4 - Phase 1 LR 1e-3 → 3e-3 - Phase 3 LR 1e-5 → 5e-6 - Dropout 0.3 → 0.4

Data Pipeline: - Train augmentation: added RandomResizedCrop, RandomHorizontalFlip, ColorJitter - Added prefetch_factor=4 to all DataLoaders

Android Deployment: - QAT (Phase 3) enabled by default (not skipped) - Auto INT8 conversion after QAT - ONNX export in Colab notebook for mobile deployment

2026-09-05 — Major Update

Training: - Added CUDA optimization: cudnn.benchmark, TF32, AMP (torch.amp), GradScaler - Added gradient clipping (max_norm=1.0) - Enabled pin_memory, persistent_workers, non_blocking transfers - Smoke test now uses real mini-dataset (5 imgs/breed) instead of 2-batch limit - Auto portable export after training completes - Better tqdm progress bars throughout

Export & Standalone Packaging: - Added portable mode: self-contained folder with model + labels + metadata - Improved progress bars on INT8 calibration - Created create_training_zip.py script to generate a clean, webapp-free training zip package - Added .gitignore configured to track memory/ while ignoring .venv/, outputs/, data/splits/, *.zip, cache files

Webapp: - Fixed argument formatting bug (--phase1_epochs → --phase1-epochs) - Fixed jobStatusHTML crash when metrics object has missing keys - Added model cache auto-invalidation after training (mtime-based) - Progress bars now show completion/error states - Running job indicator with pulse animation in header - Auto-refresh status, metrics, and exports after job completion - Added portable export option in UI dropdown

Config: - Added PORTABLE_EXPORT_DIR, SMOKE_SAMPLES_PER_BREED constants